text summarizer

python-projects/model-based/text-summarization-tool/README.md

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+# Clone the repository
+git clone your-github-repo-url text-summarization-tool
+
+# Navigate to the project directory
+cd text-summarization-tool
+
+# Set up the backend
+```bash
+cd backend
+python3 -m venv venv   # Create a virtual environment | don't follow this so that you won't have to install all the dependencies again so just source to activate the already existing pages
+source venv/bin/activate      # Activate the virtual environment (use `venv\Scripts\activate` on Windows)
+pip install -r requirements.txt  # Install required packages
+python app.py  or python3 app.py # This runs the Flask server and also downloads the model, or you can change the facebook/bart-large-cnn model to a smaller one like `t5-small`
+```
+# Set up the frontend
+cd ../frontend
+open the index.html on your browser or follow the optional step below
+
+# Optional: Use a local server to serve the frontend
+python -m http.server  # This serves the frontend at http://localhost:8000
+
+Accessing the Application
+Backend URL: http://127.0.0.1:5000 (for API requests)
+Frontend URL: http://localhost:8000 (for the web interface)
+
+Testing the Application
+Open the frontend in your browser at http://localhost:8000.
+Enter a long piece of text and click "Summarize" to test if the application works as expected.
+
+```text
+flask
+flask-cors
+transformers
+torch
+```
+Explanation of the Packages
+`flask:` The web framework for building the backend API.
+`flask-cors:` A Flask extension for handling Cross-Origin Resource Sharing (CORS), allowing your frontend to communicate with the backend.
+`transformers:` The Hugging Face library that provides the pre-trained models and pipelines, including the distilbart model.
+`torch:` The PyTorch library, which is required to run the models from the transformers library.

python-projects/model-based/text-summarization-tool/backend/.gitignore

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+venv

python-projects/model-based/text-summarization-tool/backend/app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from model.summarization import summarize_text
+from utils.helpers import validate_text, sanitize_text
+from config import Config
+
+app = Flask(__name__)
+app.config.from_object(Config)
+CORS(app, origins=app.config['ALLOWED_ORIGINS'])  # Allow CORS for specified origins
+
+@app.route('/summarize', methods=['POST'])
+def summarize():
+    data = request.json
+    text = data['text']
+
+    try:
+        # Validate and sanitize the input text
+        validate_text(text)
+        sanitized_text = sanitize_text(text)
+
+        summary = summarize_text(sanitized_text)
+        return jsonify(summary=summary)
+    except ValueError as ve:
+        return jsonify(error=str(ve)), 400
+    except Exception as e:
+        return jsonify(error=str(e)), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

python-projects/model-based/text-summarization-tool/backend/config.py

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+import os
+
+class Config:
+    """Configuration class for the application."""
+    DEBUG = True
+    ALLOWED_ORIGINS = os.getenv("ALLOWED_ORIGINS", "*")  # CORS settings
+    MAX_INPUT_LENGTH = 2000  # Maximum input length in words
+
+    # You can add more configuration variables as needed

python-projects/model-based/text-summarization-tool/backend/model/summarization.py

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+from transformers import pipeline
+
+# Load the summarization model
+summarizer = pipeline("summarization", model="t5-small")
+
+def summarize_text(text):
+    # Ensure the text is less than 1024 tokens for BART model
+    if len(text.split()) > 1024:
+        text = ' '.join(text.split()[:1024])  # Truncate to 1024 words
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']

python-projects/model-based/text-summarization-tool/backend/requirements.txt

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+Flask
+flask-cors
+transformers  # For AI model
+torch         # If you're using a PyTorch-based model

python-projects/model-based/text-summarization-tool/backend/utils/helpers.py

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+# helpers.py
+
+def validate_text(text):
+    """Validates that the input text is a non-empty string."""
+    if not isinstance(text, str) or len(text.strip()) == 0:
+        raise ValueError("Text must be a non-empty string.")
+    return True
+
+def sanitize_text(text):
+    """Sanitizes the input text by stripping whitespace."""
+    return text.strip()

python-projects/model-based/text-summarization-tool/frontend/app.js

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+document.getElementById('summarizationForm').onsubmit = async function (e) {
+    e.preventDefault();
+    const inputText = document.getElementById('inputText').value;
+
+    // Show loading indicator
+    const summaryResult = document.getElementById('summaryResult');
+    summaryResult.innerText = "Generating summary...";
+
+    try {
+        const response = await fetch('http://127.0.0.1:5000/summarize', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ text: inputText })
+        });
+
+        const summaryData = await response.json();
+        if (summaryData.error) {
+            summaryResult.innerText = "Error: " + summaryData.error;
+        } else {
+            summaryResult.innerText = summaryData.summary;
+        }
+    } catch (error) {
+        summaryResult.innerText = "Error: " + error.message;
+    }
+};

python-projects/model-based/text-summarization-tool/frontend/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Text Summarization Tool</title>
+    <link rel="stylesheet" href="style.css">
+</head>
+<body>
+    <div class="container">
+        <h1>Text Summarization Tool</h1>
+        <form id="summarizationForm">
+            <textarea id="inputText" rows="10" cols="50" placeholder="Enter your text here..."></textarea>
+            <br>
+            <button type="submit">Summarize</button>
+        </form>
+        <div id="summaryResult"></div>
+    </div>
+    <script src="app.js"></script>
+</body>
+</html>

python-projects/model-based/text-summarization-tool/frontend/style.css

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+body {
+    font-family: Arial, sans-serif;
+    background-color: #f4f4f4;
+    margin: 0;
+    padding: 20px;
+}
+
+.container {
+    max-width: 600px;
+    margin: auto;
+    background: white;
+    padding: 20px;
+    border-radius: 8px;
+    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+}
+
+h1 {
+    text-align: center;
+}
+
+textarea {
+    width: 100%;
+    padding: 10px;
+    margin-top: 10px;
+    border-radius: 4px;
+    border: 1px solid #ccc;
+}
+
+button {
+    width: 100%;
+    padding: 10px;
+    border: none;
+    background-color: #007BFF;
+    color: white;
+    border-radius: 4px;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #0056b3;
+}
+
+#summaryResult {
+    margin-top: 20px;
+    padding: 10px;
+    background: #e7f1ff;
+    border: 1px solid #007BFF;
+    border-radius: 4px;
+}

user-backend/src/main/java/com/bansikah/user/config/WebConfig.java

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 public class WebConfig implements WebMvcConfigurer {
 
     @Override
-    public void addCorsMappings(CorsRegistry registry) {
+    public void addCorsMappings(@SuppressWarnings("null") CorsRegistry registry) {
         registry.addMapping("/**")
                 .allowedOrigins("http://localhost:3000") // Adjust as needed for your React frontend
                 .allowedMethods("GET", "POST", "PUT", "DELETE", "OPTIONS");